Road Weather Tools for Integrated Modeling and Performance Management

On average, there are over 5.8 M vehicle crashes each year of which 23% are weather-related. Weather-related crashes are defined as those crashes that occur in adverse weather (i.e., rain, sleet, snow, fog, severe crosswinds, or blowing snow/sand/debris) or on slick pavement (i.e., wet pavement, snowy/slushy pavement, or icy pavement). The vast majority of most weather-related crashes happen on wet pavement and during rainfall: 74% on wet pavement and 46% during rainfall. These numbers continue to tell a story that road weather affects the safety and mobility of the traveling public. In order to mitigate these road weather impacts, decision support tools must be made available to maintenance and operations within the DOTs. Additionally, decision makers about getting on the roadways must also have decision support tools – for instance, school superintendents deciding whether it is safe to run buses; dispatchers routing trucks to make sure the cargo arrives on time; and the parent who needs to get medicine in the middle of the night. Each of these need decision support tools to make decisions about the roadways, but more importantly – they need decision support tools that will tell them the impact of the road weather.

There are two decision support tools we would like to introduce – the Integrated Model for Road Condition Prediction (IMRCP) and the Road Weather Performance Management (RW-PM) Tool.

Integrated Model for Road Condition Prediction (IMRCP)

Transportation Systems Management and Operations (TSMO) are at the cusp of revolutionary changes spurred by increasing data availability from wide-ranging sources and the sophistication of models utilizing the data. New approaches and data in road weather management, traffic management, incident management, and connected vehicles are impacting TSMO and traveler experiences. Within this context, the role of prediction and forecasting will become more important to the decisions made by operating agencies, travelers, freight carriers and logistics providers.

The purpose of the project is to conceptualize a system that integrates data sources and predictive methods to effectively predict road and travel conditions. The project will survey the existing field of predictive models and develop a concept of operations and requirements for an integrated model for predicting road conditions that incorporates transportation and non-transportation data, deterministic and probabilistic data, and measured and reported data. The model will ultimately provide a practical tool for transportation operations to support traveler advisories and maintenance and operational decisions at strategic and tactical levels. To facilitate usage by the State and local agencies, the model has to be easy to use, rely on available data sources at an agency, integrate with existing legacy systems, generate timely predictions, and ultimately provide decision support in a manner useful to operators and travelers. A future project phase will implement and deploy the demonstration system.

Road Weather Performance Management (RW-PM) Tool

The Prototype RW-PM Tool is a process that works to integrate and optimize existing or planned traffic control and road weather maintenance strategies. The intent of the RW-PM Tool is to leverage connected vehicle and other data sources to support • Integration of traffic mobility, road weather maintenance, and motorist advisory management, • Continuous real-time assessment of response effectiveness, • Continuous real-time adjustment and optimization of traffic control, RW maintenance, and motorist advisory response strategies, and • Use of traffic control and road weather maintenance strategies preferred by a DOT for its specific weather hazards and response objectives.

The Tool will be developed with data processors and data storage located in the Microsoft Azure Cloud Computing Platform. The Tool will collect data from multiple sources including connected vehicles, DOT infrastructure sensors, external subscription-based sources, and from road weather maintenance vehicles. The RW-PM Tool will generate traffic control recommendations, road treatment decisions, and motorist advisory recommendations. The information collected and the recommendations will be integrated and displayed on a website for assessment by DOT traffic control and RW maintenance managers. Upon management authorization, the recommendations will be deployed to DOT staff through internal systems and to motorists through connected vehicle systems using smartphone applications and cellular communication.

RW-PM Tool Demonstration Objectives: As part of the Prototype Deployment and Evaluation task, we will demonstrate that the Prototype RW-PM Tool has the functionality and performance to capture necessary data, process it, display relevant measures and metrics to DOT traffic control and RW maintenance managers, and, upon authorization, deploy it to drivers and end users. The demonstration will include near real-time capture of data from connected vehicles and from RW maintenance vehicles as well as periodic updates of Traffic Control and RW Maintenance Deployment Recommendations by the system as weather events evolve. To support these deployment and evaluation objectives, we anticipate that the deployment will include demonstration of each of the following RW-PM Tool processes. • Demonstrate data capture from Fixed and Mobile Data Sources o Mobile Traffic Mobility and Road Weather Data Sources such as  Connected (Passenger) Vehicles  Connected RW Maintenance Vehicles (via AVL) o Fixed Traffic Mobility Data Sources such as  Loop detectors  Inrix data o Fixed Road Weather Data Sources such as  RWIS • Demonstrate processing, integration and display of Traffic Mobility and Road Weather Data Information for viewing by o Traffic Control and Road Weather Maintenance Managers o Motorists • Demonstrate the generation of RW-PM Tool Recommendations for o Traffic Control o Motorist Advisory o RW Maintenance Dispatch • Demonstrate the review, editing (if needed), and authorization of recommendation deployment by Traffic Control and RW Maintenance Managers • Demonstrate the Deployment of Recommendations to o Traffic Control Systems o Motorist Advisory Systems o RW Maintenance Dispatch Systems • Demonstrate In-Vehicle Display of Messages and Information to o Motorists o RW Maintenance Vehicle Drivers • Demonstrate the periodic refreshment of input data and updates to recommendations throughout a weather event.